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A combined approach for machine rare failure detection and process monitoring using machine learning and multivariate control charts

Jihen Issaoui et al.

European Journal of Industrial Engineering2026https://doi.org/10.1504/ejie.2026.151921article
AJG 2
Weight
0.50

What the paper says

Predictive maintenance is a powerful tool for reducing costly interruptions in modern manufacturing. One of its challenges is proactively detecting rare machine failures, which impact equipment health and process stability despite their infrequency. This paper introduces a combined approach to predicting rare machine failures and monitoring process stability using statistical and technological techniques. Initially, a data augmentation method is used to handle imbalanced data. Then, three machine learning algorithms (gradient boosting, K-nearest neighbour, and logistic regression) are tested and compared for their performance in detecting rare machine failures. Furthermore, principal component analysis is used to establish multivariate control charts, specifically TPCA2 and Q charts, to monitor manufacturing processes and equipment behaviour. The proposed approach, tested with real-world data, has demonstrated effective results in predicting rare failures and in monitoring equipment behaviour. [Received: 28 May 2024; Accepted: 30 April 2025]

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https://doi.org/https://doi.org/10.1504/ejie.2026.151921

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@article{jihen2026,
  title        = {{A combined approach for machine rare failure detection and process monitoring using machine learning and multivariate control charts}},
  author       = {Jihen Issaoui et al.},
  journal      = {European Journal of Industrial Engineering},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ejie.2026.151921},
}

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A combined approach for machine rare failure detection and process monitoring using machine learning and multivariate control charts

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.